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1 May 2013 CFA Institute Journal Review

Cash Holdings and Credit Risk (Digest Summary)

  1. Keith Joseph MacIsaac, CFA, CIPM

Intuition tells us that cash-rich firms should have lower credit spreads and lower long-term probability of default than other firms, but numerous studies present evidence to the contrary. The authors explore this conundrum and discover that in the presence of financing restrictions, riskier firms tend to stockpile cash as a precaution.

What’s Inside?

The primary goals of the authors’ research are to demonstrate that corporate cash holdings can be positively correlated with credit spreads, to investigate the probability of default, and to offer plausible explanations to substantiate this anomaly. Accordingly, it is important to separate variations in a firm’s cash holdings that are exogenous and indirectly related to credit risk from variations in a firm’s cash holdings that are endogenous, occurring as a direct result of changes in credit risk. Examples of exogenous factors are a firm’s specific cash management policies and managerial incentives to avoid bankruptcy. Endogenous factors include a decline in a firm’s expected future cash flows or an increase in leverage.

How Is This Research Useful to Practitioners?

The focus of the authors’ research is the application of a corporate finance template in which firms must optimally choose between the amount of cash they invest and the amount they retain as a buffer for liquidity purposes. This process entails striking a balance between the opportunity costs of any forgone investments and the explicit interest rate costs embodied in credit spreads. The authors assert several hypotheses based on their model in order to explore this delicate interaction.

They begin by replicating previous studies that controlled for firm-specific risk attributes, and they make two discoveries that are both noteworthy and contradictory. First, they find that a standard deviation increase of 1 in the cash-to-assets ratio is associated with a 20 bp increase in credit spreads. Second, when testing such default-predicting models as Altman’s Z-score, they find that in the short term, increased liquidity is associated with reduced probability of default. For periods longer than one year, the reverse is true. This counterintuitive outcome is a result of firms’ precautionary motive for increasing cash in the face of increased risk. For example, if higher risk is defined as an expectation of lower cash flows, firms that must make debt payments will reallocate capital from investments to cash to reduce the risk of not having the cash for the debt payment. The direct effect of a firm’s higher risk supersedes the indirect effect of a firm’s internal policy that increases its cash holdings as a safety measure, as long as there are sufficient restrictions on obtaining external financing.

For variations in cash holdings unrelated to changes in risk factors, the authors’ model calculates the expected negative association between variations in cash and credit spreads.

Finally, when studying the association between increased liquidity and the probability of default, the authors clarify that there is a negative correlation over the short term and a positive correlation over the long term.

How Did the Authors Conduct This Research?

The authors’ focus is on the credit spreads and default probabilities of nonfinancial U.S. firms that had at least one public bond outstanding from December 1996 to September 2010. They obtain bond information from the Fixed Income Securities Database provided by Mergent, default data from Moody’s Default & Recovery Database, financial data from Compustat, executive compensation data from ExecuComp, and expected default probability estimates from Moody’s/KMV. Bond prices used to calculate yield spreads are gathered from the Merrill Lynch U.S. Investment Grade Index and the High Yield Master II Index. After the authors ensure that the bond spreads are uncontaminated, the sample includes 530 firms, 35,206 firm months, and 103,691 bond-month observations with which to calculate bond spreads.

The sample’s credit spreads have a mean of 224 bps and a median of 153 bps. Not surprisingly, the spreads increase with maturity and credit rating. Noteworthy in this regard is the fact that the average BB spread of 361 bps is nearly twice that for BBB bonds (196 bps).

Balance sheet liquidity is represented by the ratios of cash to assets, working capital to total assets, current assets to current liabilities, and current assets less inventories to current liabilities. When cash holdings by credit rating are graphed, a U-shaped pattern emerges: The highest- and lowest-rated firms have the highest cash holdings. When the authors test if this same relationship is present for all (i.e., public and nonpublic) U.S. nonfinancial bond issuers in the Compustat database, the U-shaped pattern reappears.

Abstractor’s Viewpoint

For credit analysts, the authors uncover several subtle relationships that will be helpful when analyzing companies’ credit risk. Namely, the limitations of standard methodologies in capturing the long-term effects of balance sheet liquidity on the probability of default and the distinction between the direct and indirect relationship between cash and spreads will advance the analysis. I also find it telling that despite all the industry clamor to streamline the role of credit rating agencies, the authors still find that investors attach meaningful significance to their opinions.

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